The configuration of algorithms is a laborious and difficult process. Thus, it is advisable to automate this task by using appropriate automatic configuration methods. The irace method is among the most widely used in the literature. By default, irace initializes its search process via uniform sampling of algorithm configurations. Although better initialization methods exist in the literature, the mixed-variable (numerical and categorical) nature of typical parameter spaces and the presence of conditional parameters make most of the methods not applicable in practice. Here, we present an improved initialization method that overcomes these limitations by employing concepts from the design and analysis of computer experiments with branching and nested factors. Our results show that this initialization method is not only better, in some scenarios, than the uniform sampling used by the current version of irace , but also better than other initialization methods present in other automatic configuration methods.

中文翻译：

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用于初始化自动算法配置的带有分支和嵌套因子的拉丁超立方体设计

算法的配置是一个费力且困难的过程。因此，建议使用适当的自动配置方法自动执行此任务。irace 方法是文献中使用最广泛的方法之一。默认情况下，irace 通过算法配置的统一采样来初始化其搜索过程。尽管文献中存在更好的初始化方法，但典型参数空间的混合变量（数值和分类）性质以及条件参数的存在使得大多数方法在实践中不适用。在这里，我们提出了一种改进的初始化方法，该方法通过采用具有分支和嵌套因素的计算机实验的设计和分析中的概念来克服这些限制。我们的结果表明，这种初始化方法不仅更好，在某些场景下，